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Fernow Experimental Forest site, station Compartment 8, subcompartment E, control reference stand (not harvested) in mixed hardwood forest, study of relative frequency of Tilia americana (american basswood) in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Fernow Experimental Forest (FER) contains relative frequency of Tilia americana (american basswood) measurements in percent units and were aggregated to a yearly timescale.
Florida mangrove saltmarsh reference surface soils
Site description. This data package consists of data obtained from sampling surface soil (the 0-7.6 cm depth profile) in black mangrove (Avicennia germinans) dominated forest and black needlerush (Juncus roemerianus) saltmarsh along the Gulf of Mexico coastline in peninsular west-central Florida, USA. This location has a subtropical climate with mean daily temperatures ranging from 15.4 °C in January to 27.8 °C in August, and annual precipitation of 1336 mm. Precipitation falls as rain primarily between June and September. Tides are semi-diurnal, with 0.57 m median amplitudes during the year preceding sampling (U.S. NOAA National Ocean Service, Clearwater Beach, Florida, station 8726724). Sea-level rise is 4.0 ± 0.6 mm per year (1973-2020 trend, mean ± 95 % confidence interval, NOAA NOS Clearwater Beach station). The A. germinans mangrove zone is either adjacent to water or fringed on the seaward side by a narrow band of red mangrove (Rhizophora mangle). A near-monoculture of J. roemerianus is often adjacent to and immediately landward of the A. germinans zone. The transition from the mangrove to the J. roemerianus zone is variable in our study area. An abrupt edge between closed-canopy mangrove and J. roemerianus monoculture may extend for up to several hundred meters in some locations, while other stretches of ecotone present a gradual transition where smaller, widely spaced trees are interspersed into the herbaceous marsh. Juncus roemerianus then extends landward to a high marsh patchwork of succulent halophytes (including Salicornia bigellovi, Sesuvium sp., and Batis maritima), scattered dwarf mangrove, and salt pans, followed in turn by upland vegetation that includes Pinus sp. and Serenoa repens. Field design and sample collection. We established three study sites spaced at approximately 5 km intervals along the western coastline of the central Florida peninsula. The sites consisted of the Salt Springs (28.3298°, -82.7274°), Energy Marine Center (28.2903°, -82
Baltimore Ecosystem Study polar organic contaminant integrative sampler data for pharmaceuticals and personal care products in core sites within Gwynns Falls watershed and reference sites.
An ongoing component of the Baltimore urban long-term ecological research (LTER) project (Baltimore Ecosystem Study, BES) is the use of the watershed approach and monitoring of stream water quality to evaluate the impacts of multiple chemical stressors on urban stream ecosystem functioning within Baltimore. The LTER research has focused on the Gwynns Falls watershed, which spans a gradient from highly urban, urban-residential, and suburban zones. In addition, a forested watershed serves as a reference. The long-term sampling network includes four longitudinal sampling sites along the Gwynns Falls mainstem, as well as several small (40-100 ha) watershed within or near the Gwynns Falls, providing data on water quality in different land use zones of the watersheds. Each study site is continuously monitored for discharge and is sampled weekly for water chemistry. Those data are available elsewhere on the BES website. We are interested in studying the presence and concentrations of pharmaceuticals and personal care products (PPCPs) within urban streams, and then linking these PPCPs with various stream ecosystem functions. To quantify the concentrations of PPCPs in streams, we deploy polar organic contaminant integrative samples (POCIS), which integrate all organic contaminants that pass by them in a stream over a set period of deployment. These POCIS allow us to integrate the total amount of PPCPs within a stream over a set period of time, and then to relate these concentrations with other ecosystem processes. We monitored PPCP concentration in four suburban to urban sites within the Gwynns Falls, as well as one exurban and one forested stream monitoring site. Each POCIS was deployed for two weeks at a stream monitoring site in March 2012. After completion of the sampling period, POCIS were removed from the streams and shipped on ice to the University of Nebraska for extraction and quantification of recovered compounds. This dataset includes all compounds extracted and q
GCAM Version 2 Reference Scenario with Water Constraints Downscaled with Demeter to 5-arcmin (Irrigated, Rain-fed)
<p>GCAM Version 2 Reference Scenario with Water Constraints Downscaled with Demeter to 5-arcmin resolution for year 2015 for irrigated and rain-fed GCAM crop breakout along with forest, urban, sparse, snow, shrub land classes. This run was generated for use by the `teleconnect` package (see <a href="https://github.com/IMMM-SFA/teleconnect">https://github.com/IMMM-SFA/teleconnect</a>). The following is the full README found in the zipped data resource:</p> <blockquote> <p>GCAM v5.2 to Demeter </p> <p>Title:<br> Demeter output for GCAM v5.2 with water constraints - Reference scenario</p> <p>Description:<br> Demeter run conducted using the base layer combining Mirca and Modis v6 type 5 to generate rain-fed and irrigated crops constrained to Modis crop area. GCAM projection split RockIceDesert into snow and sparse land classes.</p> <p>Building the Demeter base layer for use with GCAM allocated land classes and use types:<br> Described in the readme_gcam-reg32basin235_modis-v6-2010_mirca2000_5arcmin.pdf document the docs directory of this data archive.</p> <p>GCAM Version: https://github.com/JGCRI/gcam-core/tree/gcam-v5.2 ; https://doi.org/10.5281/zenodo.3528353 </p> <p>GCAM Reference:<br> Calvin, K., Patel, P., Clarke, L., Asrar, G., Bond-Lamberty, B., Cui, R. Y., Di Vittorio, A., Dorheim, K., Edmonds, J., Hartin, C., Hejazi, M., Horowitz, R., Iyer, G., Kyle, P., Kim, S., Link, R., McJeon, H., Smith, S. J., Snyder, A., Waldhoff, S., and Wise, M.: GCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems, Geosci. Model Dev., 12, 677–698, https://doi.org/10.5194/gmd-12-677-2019, 2019.</p> <p>Demeter Reference:<br> Vernon, C.R., Le Page, Y., Chen, M., Huang, M., Calvin, K.V., Kraucunas, I.P. and Braun, C.J., 2018. Demeter – A Land Use and Land Cover Change Disaggregation Model. Journal of Open Research Software, 6(1), p.15. DOI: http://doi.org/10.5334/jors.208</p> <p>Run:<br> GCAM reference scenario with water constraints conducted by Sonny Kim (skim@pnnl.gov) originally retrieved from PNNL's Constance here: /pic/projects/GCAM/water_market/database_basexdbGCAM51WaterConstr. </p> <p>Contents:<br> teleconnect_agu2019<br> -- config_gcam5p1_watconstr_ref.ini (Demeter configuration file) <br> -- code (code to run Demeter pre-, run, and post-processing)<br> ---- README.txt (Description of run order and process for Demeter on Constance)<br> ---- demeter_preprocess.py (Python script to extract land data from the GCAM database and split RockIceDesert into snow and sparse)<br> ---- demeter_postprocessing.py (Python script to create fractional output of Demeter's native output in square kilometers)<br> ---- run_demeter.py (Python script to run Demeter)<br> ---- run_demeter_gcam5p1_watconstr_ref.sh (sbatch script to submit a Demeter run on Constance)<br> ---- run_postprocessing.sh (sbatch script to submit a post-processing run on Constance)<br> ---- run_preprocessing.sh (sbatch script to submit a pre-processing run on Constance)<br> ---- slurm-11504861.out (Slurm output from Demeter run)<br> -- GCAM <br> ---- database_basexdbGCAM51WaterConstr (GCAM output database)<br> -- inputs (input files used by Demeter) <br> ---- allocation <br> ------ gcam_regbasin_modis_v6_type5_mirca_5arcmin_constraint_alloc.csv (weighting of constraints)<br> ------ gcam_regbasin_modis_v6_type5_mirca_5arcmin_observed_alloc.csv (reclassification table for observed land classes to Demeter final land classes)<br> ------ gcam_regbasin_modis_v6_type5_mirca_5arcmin_order_alloc.csv (processing order for land classes)<br> ------ gcam_regbasin_modis_v6_type5_mirca_5arcmin_projected_alloc.csv (reclassification table for GCAM land classes to Demeter final land classes)<br> ------ gcam_regbasin_modis_v6_type5_mirca_5arcmin_transition_alloc.csv (transition order for land classes)<br> ---- constraints<br> ------ 000_nutrientavail_hswd_5arcmin.csv (nutrient availability constraint weighted by grid cell)<br> ------ 001_soilquality_hswd_5arcmin.csv (soil quality constraint weighted by grid cell)<br> ---- observed<br> ------ gcam_reg32_basin235_modis_v6_2010_mirca_2000_5arcmin_sqdeg_wgs84_11Jul2019.csv (Demeter base layer)<br> ---- projected<br> ------gcam_5p1_watconst_reference.csv (output from demeter_preprocess.py from GCAM output)<br> ------gcam_5p1_watconst_reference_split.csv (output from demeter_preprocess.py from GCAM output with RockIceDesert split into snow and sparse land classes)<br> ---- reference (see https://github.com/IMMM-SFA/demeter)<br> ------ aezcoord.csv<br> ------ countrycoord.csv<br> ------ gcam_basin_lookup.csv<br> ------ gcam_regions_32.csv<br> ------ limits.csv<br> ------ query_land_reg32_basin235_gcam5p0.xml (land allocatio query)<br> ------ regioncoord.csv<br> -- for_teleconnect<br> ---- usa_demeter.csv (file used by the `teleconnect model` containing only 5-arcmin grid cells that are in GCAM region 1 (USA))<br> -- outputs (output files from Demeter run)<br> ---- ref_watconstr_2019-11-07_07h20m46s (output Demeter run directory)<br> ------ log_files (log file directory)<br> -------- logfile_ref_watconstr_2019-11-07_07h20m46s.log (log file from Demeter run)<br> ------ spatial_landcover_tabular<br> -------- landcover_2015_fraction.csv (fraction of land cover per grid cell per land class for 2015) <br> -------- landcover_2015_sqkm.csv (square kilometers of land cover per grid cell per land class for 2015) <br> -------- landcover_2015_timestep.csv (square kilometers of land cover per grid cell per land class for 2015) <br> -- docs <br> ---- readme_gcam-reg32basin235_modis-v6-2010_mirca2000_5arcmin.pdf (creation of the Demeter base layer)</p> </blockquote>
Reference Windfarm database PDk 90
<p>Dataset for TotalControl reference windfarm database simulation of a pressure-driven high Reynolds number boundary layer flow with 90 degree inflow wind direction angle (Casename PDk 90)</p> <p>Included Python files for loading and visualizing the data. Use the plot_*.py files.</p> <p>Further information, including description of the case and dataset can be found in the deliverable report at: </p> <p><a href="https://cordis.europa.eu/project/id/727680/results">https://cordis.europa.eu/project/id/727680/results</a></p> <p>"Database for reference wind farms part 2: windfarm simulations"</p>
Reference Windfarm database PDkhi 0
<p>Dataset for TotalControl reference windfarm database simulation of a pressure-driven high Reynolds number boundary layer flow with 0 degree inflow wind direction angle (Casename PDkhi 0)</p> <p>Included Python files for loading and visualizing the data. Use the plot_*.py files.</p> <p>Further information, including description of the case and dataset can be found in the deliverable report at: </p> <p><a href="https://cordis.europa.eu/project/id/727680/results">https://cordis.europa.eu/project/id/727680/results</a></p> <p>"Database for reference wind farms part 2: windfarm simulations"</p>
Reference Windfarm database CNk8 90
<p>Dataset for TotalControl reference windfarm database simulation of a conventionally neutral boundary layer flow with 90 degree inflow wind direction angle (Casename CNk8 90)</p> <p>Included Python files for loading and visualizing the data. Use the plot_*.py files.</p> <p>Further information, including description of the case and dataset can be found in the deliverable report at: </p> <p><a href="https://cordis.europa.eu/project/id/727680/results">https://cordis.europa.eu/project/id/727680/results</a></p> <p>"Database for reference wind farms part 2: windfarm simulations"</p>
Pytroll reference magery from the NWCSAF/Geo v2018 test dataset
<p>Imagery generated with Satpy on the test dataset of the v2018 of the NWCSAF/Geo package. The testdata set is available from www.nwcsaf.org</p>
FIGURES 3–5 in To which species should the name heynei Rühl, [1893] (Lepidoptera: Nymphalidae) be referred?
FIGURES 3–5. The neotype of Melitaea arduinna heynei Rühl, [1893], 3: upperside; 4: underside; 5: labels. (Scale bar = 10 mm).
mGEMS Staphylococcus aureus reference dataset
<p>This dataset contains the <em>S. aureus</em> sequences (assembled with shovill v0.9.0), their sequence type 22 clades, and accession numbers used in the mGEMS publication as the reference dataset.</p>
Distribution. Widely in S Africa, but N & W boundaries are not yet known; known to occur in S DR Congo, W Angola, Zambia, Malawi, Mozambique, Namibia, N Botswana, Zimbabwe, South Africa, and Swaziland. A species morphologically identical to Ä caffer occurs widely in East Africa but whether this refers to H. caffer or H. tephrus has not yet been established. in Hipposideridae
Distribution. Widely in S Africa, but N & W boundaries are not yet known; known to occur in S DR Congo, W Angola, Zambia, Malawi, Mozambique, Namibia, N Botswana, Zimbabwe, South Africa, and Swaziland. A species morphologically identical to Ä caffer occurs widely in East Africa but whether this refers to H. caffer or H. tephrus has not yet been established.
The WWU DUNEuro reference data set for combined EEG/MEG source analysis
<p>The provided dataset consists of two high-quality realistic head models and combined EEG/MEG data which can be used for state-of-the-art methods in brain research, such as modern finite element methods (FEM) to compute the EEG/MEG forward problems using the software toolbox DUNEuro (http://duneuro.org).</p> <p>A combined EEG/MEG dataset from a somatosensory experiment is provided (<strong>sep_sef.zip</strong>): Somatosensory evoked potentials (SEP) and fields (SEF) were elicited by stimulating the median nerve at the wrist of the right arm with monophasic square-wave electrical pulses with 0.5 ms duration. A random stimulus onset asynchrony between 350 and 450 ms was used and the strength was adjusted to invoke a clear movement of the thumb. The duration of the experiment was 10 minutes for a measurement of 1200 trials and data was acquired with a sampling rate of 1200 Hz and online low pass filtered at 300 Hz. An artifact reduction was achieved by reversing the polarity of the stimulation during the second half of the measurement. A 74-channel EEG (EASYCAP GmbH, Herrsching, Germany), for which the electrode positions were digitized using a Polhemus device (FASTRAK, Polhemus Incorporated, Colchester, Vermont, U.S.A.), and a whole-head MEG with 275 axial gradiometers and 29 reference coils (OMEGA2005, VSM MedTech Ltd., Canada) were used in the measurement.</p> <p>Ethics Statement: One healthy subject (49 years, male) participated in this study. The subject had no history of psychiatric or neurological disorders and had given written informed consent before the experiment. All procedures had been approved by the ethics committee of the University of Erlangen, Faculty of Medicine on 10.05.2011 (Ref. No. 4453).</p> <p>Additionally, two different advanced realistic head models are supplied, which both use a six-compartment segmentation from T1/T2-MRI of the test subject. They differentiate between scalp, skull compacta, skull spongiosa, cerebrospinal fluid (CSF) and gray and white matter tissue. One head model is a tetrahedral volumetric mesh (<strong>realistic_tet_mesh_6c.msh</strong>), while the other provides the geometric information by level-sets for each tissue boundary (<strong>realistic_levelsets_6c.zip</strong>). </p> <p>A detailed description of the construction of the tetrahedral mesh can be found <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.25272">here</a> (subsection 2.3), the main steps are presented in the following. First, the MR images were co-registered and resampled so that the voxels of the anatomical data are cubic. Furthermore, the images were cut sufficiently below the skull of the participant. Subsequently, the segmentation of the T1w and T2w was performed in order to create six volumetric masks representing the six tissue compartments. The brain compartment was segmented via the <a href="http://surfer.nmr.mgh.harvard.edu">FreeSurfer</a> software. The remaining preprocessing and creation of the volumetric masks was entirely performed via routines available in <a href="https://www.fieldtriptoolbox.org/">FieldTrip</a>. In particular, the scalp and skull segmentations were done via the <a href="https://www.fil.ion.ucl.ac.uk/spm/software/spm12/">spm12</a> software, embedded in FieldTrip. Once the masks were assembled, a volumetric tetrahedral mesh was created using the <a href="https://doc.cgal.org/Manual/3.5/doc_html/cgal_manual/Mesh_3/Chapter_main.html">CGAL</a> software embedded in <a href="http://iso2mesh.sourceforge.net/cgi-bin/index.cgi">iso2mesh</a>, resulting in 885,214 nodes and 5,335,615 tetrahedrons. The mesh is provided in <a href="https://gmsh.info">gmsh</a> format, including information about the node positions, elements defined by their node indices, and labels for each element indicating the tissue compartment.</p> <p>For the construction of the unfitted head model, a six-compartment voxel segmentation was constructed based on the T1- and T2-weighted MR images, distinguishing between skin, skull compacta and spongiosa, CSF, gray and white matter using <a href="https://www.fil.ion.ucl.ac.uk/spm/software/spm12/">SPM12</a> via <a href="https://www.fieldtriptoolbox.org/">Fieldtrip</a>, <a href="https://fsl.fmrib.ox.ac.uk/fsl">FSL</a> and internal MATLAB routines. Surfaces were extracted from this voxel segmentation to distinguish between the different tissue compartments. In order to smooth the surfaces while sustaining the available information from the segmentation, we applied an anti-aliasing algorithm created for binary voxel images presented in (<a href="https://doi.org/10.1145/353888.353893">Whitaker, 2000</a>). The resulting smoothed surfaces are represented as discrete level-set functions, i.e., by <span class="math-tex">\(N^3\)</span>-dimensional arrays (<span class="math-tex">\(N\)</span>=257), the value on each node indicates the signed distance to the respective surface.</p>
Figure 15. Cleveland tyrannosaur skull, CMNH 7541 in The Cleveland tyrannosaur skull (Nanotyrannus or Tyrannosaurus): new findings based on CT scanning, with special reference to the braincase
Figure 15. Cleveland tyrannosaur skull, CMNH 7541. Volume renderings of digitally extracted right mandible derived from CT data in A, dorsal; B, medial; C, lateral views. A–C are stereopairs. D, close-up of mandible in lateral view; numbers correspond to tooth positions, of which there are 16. Scale bars equal 10 cm.
Figure 12. Cleveland tyrannosaur skull, CMNH 7541 in The Cleveland tyrannosaur skull (Nanotyrannus or Tyrannosaurus): new findings based on CT scanning, with special reference to the braincase
Figure 12. Cleveland tyrannosaur skull, CMNH 7541. Volume (A) and surface (B) renderings of digitally extracted left palatine derived from CT data in lateral view. Both sets are stereopairs. Surface rendering (B) is partially transparent to reveal the internal pneumatic sinuses; note that the two sinuses do not communicate. Scale bar equals 2 cm. See Appendix for abbreviations.
Figure 11. Cleveland tyrannosaur skull, CMNH 7541 in The Cleveland tyrannosaur skull (Nanotyrannus or Tyrannosaurus): new findings based on CT scanning, with special reference to the braincase
Figure 11. Cleveland tyrannosaur skull, CMNH 7541. Volume (A) and surface (B) renderings of digitally extracted left quadratojugal derived from CT data in lateral view. Both sets are stereopairs. Surface rendering (B) is partially transparent to reveal the internal pneumatic sinus. In the actual specimen, the jugal process was displaced relative to the rest of the bone, but has been digitally reattached here. Scale bar equals 5 cm. See Appendix for abbreviations.
Figure 9. Cleveland tyrannosaur skull, CMNH 7541 in The Cleveland tyrannosaur skull (Nanotyrannus or Tyrannosaurus): new findings based on CT scanning, with special reference to the braincase
Figure 9. Cleveland tyrannosaur skull, CMNH 7541. Surface renderings of digitally extracted braincase derived from CT data, made partially transparent to reveal brain endocast (light blue) and internal pneumatic sinuses, in A, left lateral; B, left rostroventrolateral; C, caudoventral views. Scale bar equals 10 cm. See Appendix for abbreviations.
Figure 6. Cleveland tyrannosaur skull, CMNH 7541 in The Cleveland tyrannosaur skull (Nanotyrannus or Tyrannosaurus): new findings based on CT scanning, with special reference to the braincase
Figure 6. Cleveland tyrannosaur skull, CMNH 7541. Surface renderings of digitally extracted braincase derived from CT data in A, left lateral; B, left rostroventrolateral; C, caudal views. Figure 5A–C shows corresponding stereopairs of volume renderings. Scale bar equals 10 cm. See Appendix for abbreviations.
Figure 4. Cleveland tyrannosaur skull, CMNH 7541 in The Cleveland tyrannosaur skull (Nanotyrannus or Tyrannosaurus): new findings based on CT scanning, with special reference to the braincase
Figure 4. Cleveland tyrannosaur skull, CMNH 7541. Volume renderings of the skull derived from the CT data in A, rostral view; B, right lateral view; C, left lateral view; D, dorsal view; E, caudal view; F, ventral view. Densities corresponding to plaster have been excluded, revealing the extent of plaster restoration. Arrows in C indicate the transverse oblique plane of fracture that the specimen experienced prior to restoration. Scale bar equals 10 cm.
Figure 2. Cleveland tyrannosaur skull, CMNH 7541 in The Cleveland tyrannosaur skull (Nanotyrannus or Tyrannosaurus): new findings based on CT scanning, with special reference to the braincase
Figure 2. Cleveland tyrannosaur skull, CMNH 7541. Stereophotographs of A, close-up of left antorbital region; B, skull in rostral view; C, skull in ventral view. Orientations reflect posture with lateral semicircular canal horizontal. Scale bars equal 10 cm.
List of tissue specimens of Hipposideros spp. used for cytochrome b sequencing and phylogenetic inference, with geographical data. Voucher refers to the location and/or accession number of the voucher, and tissue collection refers to the collection where the tissue is kept, and accession or collector numbers. Acronyms are as follows: Estación Biológica de DoZana, Sevilla, Spain (EBD), South Australia Museum, Adelaide, Australia (SAM), Senckenberg Museum, Frankfurt am Main, Germany (SMF), Instituto de Ecología, Xalapa, México (IEX), Louisiana State University, Baton Rouge, Louisiana, USA (LSU), Charles M. Francis, Canadian Wildlife Service, Ottawa, Ontario, Canada (CMF), Lao Department of Forestry, Vientiane, Lao PDR, no catalogued (LAO). Next column indicates GenBank accession numbers. More information about the specimens is available in the GenBank records in A new species of bat of the Hipposideros bicolor group (Chiroptera: Hipposideridae) from Central Laos, with evidence of convergent evolution with Sundaic taxa
List of tissue specimens of Hipposideros spp. used for cytochrome b sequencing and phylogenetic inference, with geographical data. Voucher refers to the location and/or accession number of the voucher, and tissue collection refers to the collection where the tissue is kept, and accession or collector numbers. Acronyms are as follows: Estación Biológica de DoZana, Sevilla, Spain (EBD), South Australia Museum, Adelaide, Australia (SAM), Senckenberg Museum, Frankfurt am Main, Germany (SMF), Instituto de Ecología, Xalapa, México (IEX), Louisiana State University, Baton Rouge, Louisiana, USA (LSU), Charles M. Francis, Canadian Wildlife Service, Ottawa, Ontario, Canada (CMF), Lao Department of Forestry, Vientiane, Lao PDR, no catalogued (LAO). Next column indicates GenBank accession numbers. More information about the specimens is available in the GenBank records
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.